
Detecting and characterizing the effects of software changes is a fundamental component of software maintenance. Version differencing information can be used to perform version merging, infer change characteristics, produce program documentation, and guide program re-validation. Existing techniques for characterizing code changes, however, are imprecise leading to unnecessary maintenance efforts.In this paper, we introduce a novel extension and application of symbolic execution techniques that computes a precise behavioral characterization of a program change. This technique, which we call differential symbolic execution (DSE), exploits the fact that program versions are largely similar to reduce cost and improve the quality of analysis results. We define the foundational concepts of DSE, describe cost-effective tool support for DSE, and illustrate its potential benefit through an exploratory study that considers version histories of two Java code bases.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 185 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 1% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 1% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 1% |
